Distributed applications that utilizeheterogeneousblockchain systems have the potential to be widely deployed. In such applications, users from different blockchains can transact with one another throughcross-chain transactions. There are two essential features of particular relevance for those applications during cross-chain transactions: theatomicityin that either all or none of the blockchains involved confirm a cross-chain transaction, theconfidentialityin that a blockchain involved in a cross-chain transaction is only accessible for designated users. Existing cross-chain proposals have largely relied on permissioned blockchains to ensure confidentiality. However, we found that failures could occur when reading or writing information during transaction confirmations across permissioned blockchains, namely read/write (r/w) failures, which can lead to the violation of atomicity. In this paper, we propose a novel mechanism,Unity, to ensure both atomicity and confidentiality of cross-chain transactions under r/w failures by leveraging permissioned blockchains. When failures occur in reading or writing data,Unityclassifies the data into two categories based on its status - whether data is the latest version or not, and presents different solutions for atomicity. Specifically, when data is not the latest, we design a four-phase-commit protocol4pc, in which consensus on confirming or aborting a cross-chain transaction can be achieved. If data is the latest when r/w failures occur, we propose a smart contract based solution SSC. We examine the effectiveness ofUnitytheoretically and through experiments. With a failure probability of 0.7,Unityachieves$98\%$more atomic cross-chain transactions when compared with the state-of-the-art cross-chain platform, Hyperservice.

Heterogeneous blockchains are expected to be increasingly deployed in real-world applications, making cross-chain transaction confirmations essential. Currently, confirmations for cross-chain transactions are usually accomplished through an intermediary, such as a relay chain, which may well become a performance bottleneck. Sharding has been widely used to improve the blockchain throughput through parallel transaction validations by distributing transactions into multiple sub-communities, Yet, when sharding technique is directly applied over a relay chain, it results in an excessive number of cross-shard transactions, offsetting the throughput improvement.In this paper, we propose Sliver, a novel transaction distribution mechanism specifically designed for improving the relay chain throughput for the first time. We first capture and leverage the unique characterization of transaction dependency on the relay chain, and place those transactions with dependency into one shard. Consequently, this completely eliminates cross-shard transactions. However, due to the varying nature of transaction dependency, such a transaction placement can lead to a highly skewed distribution in terms of the number of transactions (i.e., shard size) to be validated in different shards, which negatively affects the relay chain throughput. We proceed to formulate the transaction distribution as an integer optimization problem with a lexicographical minimization objective for achieving a balanced shard size. While such a problem is proved to be NP-hard, we are able to mathematically transform it to a linear programming (LP) formulation by incorporating several unique properties in the integer optimization formulation, which can then be efficiently solved using off-the-shelf LP solvers. Theoretical and experimental analyses show that Sliver is extremely efficient in solving the assignment problem and the throughput can be 5 × that of the state-of-the-art under various configurations.

With their instant transaction confirmation and high scalability, payment channel networks (PCNs), running off-chain and in parallel with blockchain systems, have recently attracted a substantial amount of research attention. It has been shown that there exists a significant gap between the theoretically optimal performance and the performance achievable given the stringent privacy requirements in practice. However, it remains unclear what the fundamental performance limits and key factors involved are, which turns out to be a challenging problem due to the unique characteristics in PCNs. In this paper, we, for the first time, develop a mathematical model capturing the PCN performance, and examine the impact from a number of factors including channel capacity and transactions. We are articularly interested in obtaining the gap between the theoretically optimal performance and the performance achievable in practice, which characterizes the design space in PCNs for scheduling transactions. Specifically, we derive how different transactions and channel capacities affect the PCN performance and the performance gap. Our analytical characterization of PCNs offers an in-depth understanding on their fundamental trade-off, and provides important insights on the design of PCNs.

Current blockchain systems suffer from a number of inherent drawbacks in its scalability, latency, and processing throughput. By enabling parallel confirmations of transactions, sharding has been proposed to mitigate these drawbacks, which usually requires frequent communication among miners through a separate consensus protocol.In this paper, we propose, analyze, and implement a new distributed and dynamic sharding system to substantially improve the throughput of blockchain systems based on smart contracts, while requiring minimum cross-shard communication. Our key observation is that transactions sent by users who only participate in a single smart contract can be validated and confirmed independently without causing double spending. Therefore, the natural formation of a shard is to surround one smart contract to start with. The complication lies in the different sizes of shards being formed, in which a small shard with few transactions tends to generate a large number of empty blocks resulting in a waste of mining power, while a large shard adversely affects parallel confirmations. To overcome this problem, we propose an inter-shard merging algorithm with incentives to encourage small shards to merge with one another and form a larger shard, an intra-shard transaction selection mechanism to encourage miners to select different subsets of transactions for validation, as well as a parameter unification method to further improve these two algorithms to reduce the communication cost and improve system reliability.We analyze our proposed algorithms using the game theoretic approach, and prove that they converge to a Nash Equilibrium. We also present a security analysis on our sharding design, and prove that it resists adversaries who occupy at most 33% of the computation power. We have implemented our designs on go-Ethereum 1.8.0 and evaluated their performance using both real-world blockchain transactions and large-scale simulations. Our results show that throughput has been improved by 7.2×, and the number of empty blocks has been reduced by 90%.

In order to achieve more dramatic spatial multiplexing gains, both industry and academia have pushed towards the massive Multi-User Multi-Input and Multi-Output (MU-MIMO) systems. However, traditional linear precoding techniques do not scale up well with the number of antennas, i.e., they either have high implementation difficulties (zero-forcing) or sacrifice wireless capacity as a price (conjugate or codebook-based precoding). In this paper, we present a novel precoding scheme, BeamRaster, which is a fast and high efficient scheme for massive MU-MIMO system. Inspired from the codebook-based precoding, BeamRaster pre-computes a set of angle-domain beam filters that divide the channel into directional subspaces. Unlike previous work, BeamRaster carefully manages the cross-interference using (1) a grating table to track the correlation among beams in real-time, (2) an interference-aware user-beam selection, and (3) a pre-distortion method to cancel the residual interference because of side-lobes. We implement and evaluate the BeamRaster using FPGA and software defined radio platform. On one hand, BeamRaster is easy to implement in hardware, i.e., it can realize the precoding for a 64-antenna MU-MIMO system in real time with a single Altera Stratix V FPGA. On the other hand, both the experiments with medium-scale antennas and simulations with large-scale antennas show that BeamRaster can achieve high capacity gain.

BBR is a congestion-based congestion control algorithm recently proposed by Google. It proactively measures the bottleneck bandwidth and round trip times (RTTs) of a connection pipe, based on which it governs its sending behaviors. Despite the significant throughput gains and latency reduction, some experimental studies reveal that BBR may result in a salient RTT-fairness problem, in that short-RTT flows can be starved of bandwidth allocation when comnetina with lons-R'I'T flows. In this paper, we study BBR's RTT-fairness problem from a theoretic perspective. We present a closed-form solution that characterizes the intrinsic dynamics of BBR flows and their interactions. Specifically, we model BBR's sending behaviors and bandwidth dynamics, based on which we establish an exponential relationship between the flows' bandwidth shares and their RTTs. We show that the degree of unfairness is dictated by the RTT ratio between two flows, irrespective of the other network parameters, such as the initial sending rates or link capacity. In particular, when the RTT ratio of the two flows is greater than 2, the short-RTT flow is starved of bandwidth allocation ( ≤ 0.1%), Our theoretical results are corroborated by simulations in a wide range of settings.